Method and apparatus for classification of pixels in medical imaging

ABSTRACT

An apparatus and method for medical imaging, particularly for mammography, wherein a body organ, such as a breast, is exposed to X-rays and the X-rays are collected after attenuation through the object. The recorded attenuations are processed and displaying a result of this processing in the form of a representation of an image of the object. The processing of the recorded attenuations form includes automatic classification of zones of the breast into pathological or non-pathological classes. The automatic classification takes into account at least one classification input into the apparatus in advance in association with data that can be collected by the apparatus, and using this prior classification as a reference in order to produce a classification of the same type if there is similarity between the collected data and the data associated with this reference classification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of a priority under 35 USC119(a)-(d) to-French Patent Application 04 05524 filed May 21, 2004, theentire contents of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

An embodiment of the invention relates to a method and apparatus formedical imaging and in particular to a method and apparatus for theclassification of pixels in medical imaging. An embodiment of theinvention relates to Contrast Medium-enhanced Mammography (CMM) byX-rays and the injection of a contrast medium.

Mammography is medical imaging intended particularly for the detectionof tumors by examination of successive images taken to reveal thevariation with time of impregnation of the contrast medium and itsgradual disappearance. In mammography the contrast medium tends toattenuate X-rays significantly more than a non-impregnated tissue, andthus reveals particularly vascularised zones such as tumors. But thevariation of contrast within the breast itself provides an importantindication about whether or not tumors are present, by the rate at whichthis contrast appears and disappears.

At present, contrast medium-enhanced mammography is practiced within thecontext of MRI, a technique that comprises making molecules composingthe examined organ vibrate. Within this context, the variation ofcontrast in the breast is displayed on the screen in the form of asequence of images that the practitioner interprets based on experience,as revealing or not revealing the presence of a tumor.

Marx et al., “Contrast-enhanced digital mammography (CEDM): phantomexperiment and first clinical results”, Proc. SPIE—International Soc.for Optical Engineering, vol. 4682, pp. 174-181, 2002, proposes toproduce maps representing the distribution of some parameters in thebreast. These parameters are measurements illustrating some kineticaspects of contrast variation obtained from a sequence of X-ray images.

However, the diagnosis work to be done by the practitioner is stillconsiderable.

BRIEF DESCRIPTION OF THE INVENTION

According to an embodiment of the invention, an apparatus comprisesmeans for exposing an object, such as a body organ, e.g., the breast, toa source of radiation, such as an X-ray beam; means for collecting theradiation after attenuation through the object; means for processingrecorded attenuations and means for displaying the result of thisprocessing in the form of a representation on an image of the object.The means for processing the recorded attenuations form means forautomatic classification of zones of the object into pathologicalclasses The means for automatic classification is suitable for takingaccount of at least one classification input into the apparatus inadvance in association with data that can be collected by the apparatus,and using this prior classification as a reference in order to produce aclassification of the same type if there is similarity between thecollected data and the data associated with this referenceclassification.

An embodiment of the invention is a method for medical imaging,particularly for mammography, comprising exposing an object, such as abody organ, e.g., a breast, to radiation; collecting the radiation afterattenuation through the object; processing recorded attenuations anddisplaying the result of this processing in the form of a representationon an image of the breast, the processing of the recorded attenuationscomprises automatically classifying zones of the object intopathological classes, the automatic classification taking into accountat least one prior classification input in association with data thatcan is collected, and using this prior classification as a reference inorder to produce a classification of the same type if there issimilarity between the collected data and the data associated with thisreference classification.

BRIEF DESCRIPTION OF THE DRAWINGS

Other characteristics, purposes and advantages of the invention willbecome clear after reading the detailed description given below withreference to the attached figures among which:

FIG. 1 shows a time axis representing different instants at which imagesare taken during impregnation/deimpregnation of a contrast medium;

FIG. 2 shows the variation of a grey level measured duringimpregnation/deimpregnation of a breast by the contrast medium;

FIG. 3 is a diagram showing a distribution of points in a severaldimensional space used for identification of a classification of a localzone of the breast; and

FIG. 4 is a time axis illustrating the use of two photos with twodifferent energies in an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the invention is to improve the way in which thepractitioner is assisted in using X-rays for contrast medium enhancedmammography, in diagnosing the presence of a particular pathology, andparticularly for identification of the presence of malignant tumors

In this description, the term “grey level” will denote a valuerepresenting as possible the attenuation recorded in the presence of thecontrast medium. In practice, these values are obtained afterapplication of a logarithm to the attenuation actually recorded, sincein the known manner the attenuation induced by the presence of acontrast medium, typically a product containing iodine, is exponentialto the local concentration of the product. The logarithm thus appliedoutputs a value approximately proportional to the attenuation due to theproduct containing iodine after passing through the breast, in otherwords the thickness actually impregnated by the product containingiodine.

In a first variant, each point on the image (or pixel) of the examinedbreast is associated with a vector in n dimensions, in which eachdimension corresponds to a different observation instant of this samepixel. In other words, this vector associated with each pixel representsthe rate at which the contrast appears in this particular pixel.

Thus, for each point located at the same location in each successiveimage during impregnation/disappearance of the contract medium, there isa vector X_(i,j) associated with this point for which each of thecomponents G_(n)(i,j) correspond to the grey level recorded at eachsuccessive instant. N is the number of successive sequences, and i,j arethe coordinates of the same pixel in each successive image in thesequence of images.

The result is thus a vector X_(i,j) defined as follows:$X_{i,j} = \begin{Bmatrix}{G_{1}\left( {i,j} \right)} \\{\vdots\quad 10} \\{G_{n}\left( {i,j} \right)}\end{Bmatrix}$

Therefore the coefficients of this vector are distributed from G₁ toG_(n)(i,j) and are representative of grey levels obtained in instants t₁to t_(n).

The first variant uses these vectors X_(i,j) to identify a similaritybetween them and vectors representing a typical variation in contrastwith time in the presence of a specific pathology. More generally, theobjective is to sort the different vectors corresponding to differentpoints into classes that could reveal the existence of some pathologies.

In one embodiment, these various vectors are classified into fourcategories. A first category comprises vectors that could reveal thepresence of a malignant tumor at the pixel i,j considered. A secondcategory comprises the vectors that could indicate the presence of abenign tumor at the pixel i,j considered. A third category comprisesvectors that could indicate the presence of healthy tissue (parenchyma)at the pixel i,j considered. A fourth category comprises vectors thatcould indicate the presence of a blood vessel at the pixel i,jconsidered.

In another embodiment, with the purpose of detecting tumors, the firstand second classification categories (malignant tumors and benigntumors) may be coincident.

The various categories may also be distributed into vessels, tumors, andnormal tissue, for detection purposes.

The following processing can be applied in order to determine which ofthese categories is applicable.

Each vector may be considered to belong to an n dimensional space, inwhich each dimension represents a given instant. The position of thepoint according to this dimension then represents the value of the greylevel observed at the instant corresponding to this dimension. This typeof space is shown in FIG. 3, in two dimensions to simplify theillustration. Therefore, these two dimensions correspond to two imagesat two different instants. A vector X_(i,j) will be located on a medianat 45° if the values of the grey levels are the same at instants t₁ andt₂.

In this case, the instant t₁ was an image instant at which no contrastmedium had yet been impregnated in the breast, and it can be understoodthat the point would belong to, the oblique line at 45° if the contrastmedium were not present at instant t₂ either. These points located onthe oblique may also be located on zones of the breast in whichvascularization is observed to be negligible or non-existent. The pointsthus positioned are classified as “parenchyma” in FIG. 3.

Thus, FIG. 2 shows the variation in the grey level as a function oftime, depending on whether the point at which this grey level isobserved forms part of a common tissue (parenchyma), a vessel, amalignant tumor or a benign tumor:

On the other hand, a vector X_(i,j) will be positioned further abovethis oblique when the impregnation at time t₂ is greater.

Two oblique bands 10 and 20 are shown, one band 10 close to the medianpassing through the origin, and the other 20 further towards the top.Therefore, the low band 10 represents a location in the breast in whichthe impregnation at time t₂ is relatively low. Therefore, the highestband 20 represents locations within the breast at which impregnation arealready very high at time t₂.

It is considered that points with low impregnation at time t₂ (low band10) correspond to the presence of a tumor, while points with highimpregnation at time t₂ (high band 20) correspond to the presence of avessel at the point considered.

It should be noted now that it is known that malignant lesions/tumorscause a very fast increase in the contrast, followed by a constantperiod, and then fast disappearance of the contrast. It should be notedalso that benign lesions/tumors are marked by a gradual increase in thecontrast. It should be noted also that vessels are obviously affected byfast contrast variations. Other tissues are less sensitive to contrastvariations.

When considering a number n of successive images, the same processing isperformed but this time in a space with n dimensions. The zonescorresponding to the different classification categories are then zonesin this space with n dimensions.

In one embodiment, the vectors thus localized on particularclassification zones are preferably vectors obtained afterpreprocessing. One desirable preprocessing comprises subtracting usingan initial vector corresponding to an image taken without the presenceof a contrast medium (this initial image is called the mask). Forexample, another type of preprocessing may comprise noise eliminationfiltering.

The classification may also be made on normalized data to compare imagesequences acquired under different conditions. Data may be normalized tocompensate for radiation conditions at different energies. Data may alsobe normalized to compensate for a variable breast thickness.

Additional components may also be provided in the vector, such as thenumber of sign changes in recorded grey levels during the imagesequence, or such as the patient's age, weight or any other data relatedto the patient's medical history. This data is also integrated in the ndimensional space, each time in the form of an additional dimensionsubsequently used for determining classifications.

In one variant, the vector X_(i,j) also includes the coordinates of thepixel considered in space. This embodiment can avoid incoherentclassification variations such as sudden classification changes innearby pixels.

In another embodiment, the dimensions of the classification space do notnecessarily correspond to a sequence of measurement instants. Eachdimension is dedicated to positioning in this space of a value of akinetic parameter calculated on the contrast variation. Thus, one of thedimensions can be dedicated to the maximum recorded value of the slopewhile determining the contrast at the pixel considered. Anotherdimension can represent the maximum value of the contrast recorded atthe same pixel considered. Another dimension can represent the holdduration of the maximum contrast at the same pixel considered.

In this variant, the m parameters thus represented in the n dimensionalspace can easily be compared with data from previous sequences ofimages, including when these images were taken at different times, inother words at different number of times t₁ . . . t_(n) or with avariable distribution in time.

Thus, FIG. 1 shows two image sequences (corresponding to the uppertriangles and the lower triangles respectively) that can be comparedmore easily because these kinetic parameters have been produced,although the images were not taken at the same instants.

According to a second embodiment of the invention, the space in whichthe vectors X_(i,j) are shown is a two dimensional space, in which thesetwo dimensions correspond to different radiation energies used atdifferent times or at the same time. In this variant, the two instantsare preferably very close to each other, in other words in practice asclose as possible.

This embodiment provoked a contrast difference between these two images,due either to a different reaction of the same dose of the contrastmedium facing two different radiation energies. For example, one of theradiations is located at about 25 to 35 keV, while the other is about 40to 49 keV. Thus advantage is taken that a contrast medium, typically aproduct containing iodine, has a capacity to attenuate X-rays thatvaries as a function of the energy in the rays passing through it.

It is known that the attenuation coefficient p varies as a function ofthe energy of the X-rays according to a variation law by which the valueof μ suddenly changes at a precisely determined energy, this suddenchange currently being called the K-edge. Thus, when the two energiesare located on the opposite sides of this K-edge, the difference incontrast is particular high between the two acquisitions.

Consequently, at pixels in a position corresponding to a strong presenceof a substance containing iodine, the contrast will be sensitive to thevariation of energy between the two images. On the other hand, zoneswithout this impregnation will only have a small reactivity to theenergy variation.

These two acquisitions, preferably very close, are more generally madeat an optimum instant for observing such contrasts and theirdifferences, after the injection of the contrast medium. Thus in thisapproach, the kinetic acquisition is replaced by a double energyacquisition, the two images being acquired at different radiationspectra (and therefore at different energies). One of the spectraadvantageously corresponds to a normal energy level for a conventionalmammographic examination, the other spectrum for example being aspectrum typically used in the context of an enhanced contrast method.

The contrast for pixels with a low impregnation will be similar at timest, and t₂, and will produce vectors X_(i,j) close to the oblique at 45°passing through the origin. Pixels i, j with strong impregnation willcorrespond to vectors X_(i,j) well above the oblique.

The variable height position of the vectors X_(i,j) makes it possible toclassify them in different zones depending on the classificationcategory mentioned above to which they belong, if any. Consequently,images taken at instants t₁ and t₂ within the kinetic of theimpregnation/deimpregnation reaction are particularly revealing of thedifferent categories.

In the above, we described the application of two radiations atdifferent energies chosen to be on each side of the sudden change in theattenuation coefficient. However, this approach is also possible even ifthe two energies are not on opposite sides of the K-edge. Thus, acontrast difference can also be used when it is due to the continuousvariation of the attenuation coefficient as a function of the radiationenergy, in other words when the two energies chosen are located in thetypical part of the variation of the attenuation coefficient, and not onopposite sides of the K-edge.

Double energy acquisitions may be carried out many times while thecontrast is increasing/reducing, and be analyzed in a space with 2ndimensions like the spaces mentioned above. Recommendations for spatialconsistency, the use of data applicable to the patient, pre-processingof vectors, normalization of data, use of kinetic parameters derivedfrom the variation in contrast differences, may also be applicable inthis “double energy” variant.

We will now describe the operation of a means for processing capable ofmaking the classification in one of the spaces with two dimensions or ndimensions described above. This means for processing are means capableof acquiring reference data used subsequently for automatic productionof the classification. To achieve this, this means (apart fromconventional data processing equipment) could implement a network ofneurons or a machine with support vectors. This means will use initialinformation input into the system as a reference result. Thisinformation is defined as reference information preferably containsvectors X_(i,j) like those defined above that can be used in theclassification space with n or with m dimensions. Therefore, mean forclassification are intended to be able to input vectors that can be usedaccording to any one of the disclosed embodiments, and take account inthe device of the fact that these input vectors correspond to a pixelbelonging to one of the classification categories.

A first operating mode comprises learning or training in the means forprocessing by inputting a collection of test data with predefined andassociated classifications, in a preliminary phase. Thus, in a firstembodiment, there are distinct implementation steps for the apparatusand method performed at different times. One step comprises acquisitionof learning data. Another step is how to use the apparatus, in otherwords, application of learning acquired on specific acquisitions.

In the variant in which the means for processing use vectors comprisingsuccessive grey levels, the training vectors will comprise a series ofsuccessive grey levels at the pixels considered. Each of these vectorsis associated with the data according to which the corresponding pixelbelongs to one of the classification categories, in a predefined manner.

A vector encountered afterwards will be categorized as belonging to thesame class as one of the reference vectors if it is similar to thisreference vector, for example, at a distance less than a predeterminedthreshold in the n dimensional space.

The same approach will be applied in the case in which the vectorcomprises kinetic parameters derived from successive grey levels, inother words, parameters such as the slope or the maximum grey level.

This learning is also applicable in the case of vectors representingdouble energy images. The reference vectors (in this case learningvectors) include the results of two contrast readings with differentenergy for examinations carried out later and the classification resultsassigned by a visual diagnosis made on these readings by a practitioneror by a laboratory analysis.

According to one variant, means for automatically establishing aclassification of zones encountered are provided, while remaining underthe guidance of the practitioner. In this approach, the means forprocessing displays the sequence of images produced. The practitionerexamines the sequence of images and identifies at least one zonerepresentative of each class, by experience. The means for processinguses these manual identifications to compare the remainder of the imagewith the zones thus classified. If the image sequence reveals otherzones that appear similar to those identified by the practitioner, thenthe method and apparatus classifies these zones in the same categoriesthat the practitioner selected for the zones used as reference.

This similarity is identified in the same way as described above, usingvectors associated with pixels identified by the practitioner asreference data. The method and apparatus displays the zones consideredas being similar and submits this result to the practitioner. In thiscase, the reference data defining the classes are at least partlydefined directly by the practitioner.

In another operating mode, the method and apparatus combined the twoapproaches mentioned above. In this case, the means for processing makesautomatic classification starting from a learning done earlier. Theresult is displayed on the screen in the form of a map identifying thedifferent zones corresponding to the different classes. In a furtherstep, the user confirms or contradicts the classification made on thesedifferent zones. The means for processing takes into account thisconfirmation or contradiction made by the practitioner. The method andapparatus integrates data learned earlier and the information comprisingdata reclassified by the practitioner, when a new automaticclassification is necessary.

The processing may then be repeated on the same sequence starting fromthe learning thus updated. In other words, the means for learning meansis reactivated after a first automatic classification to includeadditional learning data like those introduced by the practitioner inthe form of confirmations or contradictions of the first result.

The various means described above, for which a classification will beautomatically output, may for example be used under the control ofsoftware capable of carrying out the various processing steps when it isimplemented on an appropriate processor.

Obviously, the various arrangements or processing described above, andothers comprising improvements thereof, can be combined differently ineach of the disclosed embodiments to achieve the same result.

One skilled in the art may make or propose various modifications to thestructure/way and/or function and/or results and/or steps of thedisclosed embodiments and equivalents thereof without departing from thescope and extant of the invention.

1. An apparatus for medical imaging comprising: means for exposing anobject to radiation; means for collecting the radiation afterattenuation through the object; means for processing recordedattenuations; and means for displaying a result of the processing in theform of a representation on an image of the object wherein the means forprocessing the recorded attenuations forms means for automaticclassification of zones of the object into pathological ornon-pathological classes, the means for automatic classification takingaccount of at least one classification input into the apparatus inadvance in association with data that can be collected by the apparatus,and using this prior classification as a reference in order to produce aclassification of the same type if there is similarity between thecollected data and the data associated with this referenceclassification.
 2. The apparatus according to claim 1 wherein the meansfor automatic classification makes a classification of pixels of animage of the object at the same location in a several dimensional space,the dimensions of this space each corresponding to the value of a greylevel at a given instant of a contrast kinetic at the pixel considered.3. The apparatus according to claim 1 wherein the means for automaticclassification makes a classification of pixels of an image of theobject at the same location in a several dimensional space, thedimensions of this space each representing a parameter among the maximumslope of a contrast variation, the value of a maximum contrast reached,a hold duration of the contrast at its maximum value, at the pixelconsidered.
 4. The apparatus according to claim 2 wherein the means forautomatic classification makes a classification of pixels of an image ofthe object at the same location in a several dimensional space, thedimensions of this space each representing a parameter among the maximumslope of a contrast variation, the value of a maximum contrast reached,a hold duration of the contrast at its maximum value, at the pixelconsidered.
 5. The apparatus according to claim 1 wherein the means forautomatic classification makes a classification of pixels of an image ofthe object at the same location, in a space with at least twodimensions, these two dimensions each representing the measured signalfor two different radiation energies at the pixel considered.
 6. Theapparatus according to claim 2 wherein the means for automaticclassification makes a classification of pixels of an image of theobject at the same location, in a space with at least two dimensions,these two dimensions each representing the measured signal for twodifferent radiation energies at the pixel considered.
 7. The apparatusaccording to claim 3 wherein the means for automatic classificationmakes a classification of pixels of an image of the object at the samelocation, in a space with at least two dimensions, these two dimensionseach representing the measured signal for two different radiationenergies at the pixel considered.
 8. The apparatus according to claim 4wherein the means for automatic classification makes a classification ofpixels of an image of the object at the same location, in a space withat least two dimensions, these two dimensions each representing themeasured signal for two different radiation energies at the pixelconsidered.
 9. The apparatus according to claim 1 wherein the means forautomatic classification takes account of at least one manualclassification of a current image zone of the object as referenceclassification; and automatically establishes the same classification incase of similarity with other data collected on the same object.
 10. Theapparatus according to claim 1 wherein the means for automaticclassification includes means for learning; and carrying out recording acollection of reference information comprising several referenceclassifications associated with data that can be collected by theapparatus.
 11. The apparatus according to claim 1 comprising: means forenabling a user to confirm or contradict an automatic classificationmade by the apparatus; and means for taking account of this confirmationor contradiction in order to incorporate the results of thisconfirmation or contradiction as a reference classification.
 12. Theapparatus according to claim 1 wherein the means for classificationidentifies incoherent spatial variations in the classification, andmodifying the classification of some locations in case of suchincoherent spatial variations.
 13. The apparatus according to claim 1comprising means for accounting of one item of data among the number ofchanges in the sign of the variation in grey levels, the age of theobject, weight or medical data, when creating a classification.
 14. Amethod for medical imaging comprising: exposing an object to radiation;collecting the radiation after attenuation through the object;processing recorded attenuations comprising automatic classifying ofzones of the object into pathological or non-pathological classes, theautomatic classification taking account of at least one classificationinput in advance in association with data that can be collected, andusing this prior classification as a reference in order to produce aclassification of the same type if there is similarity between thecollected data and the data associated with this referenceclassification; and displaying a result of the processing in the form ofa representation on an image of the object the process.
 15. The methodaccording to claim 14 wherein the automatic classification makes aclassification of pixels of an image of the object at the same locationin a several dimensional space, the dimensions of this space eachcorresponding to the value of a grey level at a given instant of acontrast kinetic at the pixel considered.
 16. The method according toclaim 15 wherein the automatic classification makes a classification ofpixels of an image of the object at the same location in a severaldimensional space, the dimensions of this space each corresponding tothe value of a grey level at a given instant of a contrast kinetic atthe pixel considered.
 17. The method according to claim 16 wherein theautomatic classification makes a classification of pixels of an image ofthe object at the same location in a several dimensional space, thedimensions of this space each representing a parameter among the maximumslope of a contrast variation, the value of a maximum contrast reached,a hold duration of the contrast at its maximum value, at the pixelconsidered.
 18. The method according to claim 14 wherein the automaticclassification makes a classification of pixels of an image of theobject at the same location, in a space with at least two dimensions,these two dimensions each representing the measured signal for twodifferent radiation energies at the pixel considered.
 19. The methodaccording to claim 15 wherein the automatic classification makes aclassification of pixels of an image of the object at the same location,in a space with at least two dimensions, these two dimensions eachrepresenting the measured signal for two different radiation energies atthe pixel considered.
 20. The method according to claim 16 wherein theautomatic classification makes a classification of pixels of an image ofthe object at the same location, in a space with at least twodimensions, these two dimensions each representing the measured signalfor two different radiation energies at the pixel considered.
 21. Themethod according to claim 17 wherein the automatic classification makesa classification of pixels of an image of the object at the samelocation, in a space with at least two dimensions, these two dimensionseach representing the measured signal for two different radiationenergies at the pixel considered.
 22. The method according to claim 14wherein the automatic classification (10, 20) takes account of at leastone manual classification of a current image zone of the object asreference classification; and automatically establishes the sameclassification in case of similarity with other data collected on thesame object.
 23. The method according to claim 14 wherein the automaticclassification includes learning; and carrying out recording acollection of reference information comprising several referenceclassifications associated with data that can be collected by theapparatus.
 24. The method according to claim 14 comprising: enabling auser to confirm or contradict an automatic classification; and takinginto account of this confirmation or contradiction in order toincorporate the results of this confirmation or contradiction as areference classification.
 25. The method according to claim 14 whereinthe classification identifies incoherent spatial variations in theclassification, and modifying the classification of some locations incase of such incoherent spatial variations.
 26. The method according toclaim 14 comprising: accounting for one item of data among the number ofchanges in the sign of the variation in grey levels, the age of theobject, weight or medical data, when creating a classification.
 27. Acomputer program comprising program code means for implementing themethod according to claim
 14. 28. A computer program product comprisinga computer useable medium having computer readable program code meansembodied in the medium, the computer readable program code meansimplementing the method according to claim
 14. 29. An article ofmanufacture for use with a computer system,.the article of manufacturecomprising a computer readable medium having computer readable programcode means embodied in the medium, the program code means implementingof the method according to claim
 14. 30. A program storage devicereadable by a machine tangibly embodying a program of instructionsexecutable by the machine to perform the method according to claim 14.